# Kernel cdf matlab

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## Kernel Distribution

This example shows how to estimate the cumulative distribution function CDF from data in a nonparametric or semiparametric way. It also illustrates the inversion method for generating random numbers from the estimated CDF. These functions allow you to generate random inputs for a wide variety of simulations, however, there are situations where it is necessary to generate random values to simulate data that are not described by a simple parametric family.

The toolbox also includes the functions pearsrnd and johnsrndfor generating random values without having to specify a parametric distribution from which to draw--those functions allow you to specify a distribution in terms of its moments or quantiles, respectively. However, there are still situations where even more flexibility is needed, to generate random values that "imitate" data that you have collected even more closely.

In this case, you might use a nonparametric estimate of the CDF of those data, and use the inversion method to generate random values. The inversion method involves generating uniform random values on the unit interval, and transforming them to a desired distribution using the inverse CDF for that distribution.

From the opposite perspective, it is sometimes desirable to use a nonparametric estimate of the CDF to transform observed data onto the unit interval, giving them an approximate uniform distribution.

This example illustrates some smoother alternatives, which may be more suitable for simulating or transforming data from a continuous distribution.

For the purpose of illustration, here are some simple simulated data. There are only 25 observations, a small number chosen to make the plots in the example easier to read. The data are also sorted to simplify plotting. The ecdf function provides a simple way to compute and plot a "stairstep" empirical CDF for data. This estimate is useful for many purposes, including investigating the goodness of fit of a parametric model to data.

Its discreteness, however, may make it unsuitable for use in empirically transforming continuous data to or from the unit interval.

It is simple to modify the empirical CDF to address that problems. Use the output of ecdf to compute those breakpoints, and then "connect the dots" to define the piecewise linear function. Because ecdf deals appropriately with repeated values and censoring, this calculation works even in cases with more complicated data than in this example. This piecewise linear function provides a nonparametric estimate of the CDF that is continuous and symmetric.

Evaluating it at points other than the original data is just a matter of linear interpolation, and it can be convenient to define an anonymous function to do that. You can use the same calculations to compute a nonparametric estimate of the inverse CDF.

Evaluating this nonparametric inverse CDF at points other than the original breakpoints is again just a matter of linear interpolation. For example, generate uniform random values and use the CDF estimate to transform them back to the scale of your original observed data.

This is the inversion method. Notice that this histogram of simulated data is more spread out than the histogram of the original data. This is due, in part, to the much larger sample size--the original data consist of only 25 values. But it is also because the piecewise linear CDF estimate, in effect, "spreads out" each of the original observations over an interval, and more so in regions where the individual observations are well-separated.

For example, the two individual observations to the left of zero correspond to a wide, flat region of low density in the simulated data.

FRM: Normal probability distribution

In contrast, in regions where the data are closely spaced, towards the right tail, for example, the piecewise linear CDF estimate "spreads out" the observations to a lesser extent.

In that sense, the method performs a simple version of what is known as variable bandwidth smoothing.Documentation Help Center. Define the input vector x to contain the values at which to calculate the cdf. Compute the cdf values for the standard normal distribution at the values in x.

Each value in y corresponds to a value in the input vector x. For example, at the value x equal to 1, the corresponding cdf value y is equal to 0. Alternatively, you can compute the same cdf values without creating a probability distribution object. Compute the cdf values for the Poisson distribution at the values in x. For example, at the value x equal to 3, the corresponding cdf value y is equal to 0. Create three gamma distribution objects.

The first uses the default parameter values. Create a plot to visualize how the cdf of the gamma distribution changes when you specify different values for the shape parameters a and b.

Fit Pareto tails to a t distribution at cumulative probabilities 0. Probability distribution name, specified as one of the probability distribution names in this table. Values at which to evaluate the cdf, specified as a scalar value or an array of scalar values. If one or more of the input arguments xABCand D are arrays, then the array sizes must be the same. In this case, cdf expands each scalar input into a constant array of the same size as the array inputs. See 'name' for the definitions of ABCand D for each distribution.

Example: [0. Data Types: single double. First probability distribution parameter, specified as a scalar value or an array of scalar values.Documentation Help Center.

In some situations, you cannot accurately describe a data sample using a parametric distribution. Instead, the probability density function pdf or cumulative distribution function cdf must be estimated from the data. A kernel distribution produces a nonparametric probability density estimate that adapts itself to the data, rather than selecting a density with a particular parametric form and estimating the parameters. This distribution is defined by a kernel density estimator, a smoothing function that determines the shape of the curve used to generate the pdf, and a bandwidth value that controls the smoothness of the resulting density curve.

Similar to a histogram, the kernel distribution builds a function to represent the probability distribution using the sample data. But unlike a histogram, which places the values into discrete bins, a kernel distribution sums the component smoothing functions for each data value to produce a smooth, continuous probability curve.

The following plot shows a visual comparison of a histogram and a kernel distribution generated from the same sample data. A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin. Because of this bin count approach, the histogram produces a discrete probability density function.

This might be unsuitable for certain applications, such as generating random numbers from a fitted distribution. Alternatively, the kernel distribution builds the probability density function pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.

For more general information about kernel distributions, see Kernel Distribution. For information on how to work with a kernel distribution, see Using KernelDistribution Objects and ksdensity. An empirical cumulative distribution function ecdf estimates the cdf of a random variable by assigning equal probability to each observation in a sample. Because of this approach, the ecdf is a discrete cumulative distribution function that creates an exact match between the ecdf and the distribution of the sample data.

The following plot shows a visual comparison of the ecdf of 20 random numbers generated from a standard normal distribution, and the theoretical cdf of a standard normal distribution.

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The circles indicate the value of the ecdf calculated at each sample data point. The dashed line that passes through each circle visually represents the ecdf, although the ecdf is not a continuous function. The solid line shows the theoretical cdf of the standard normal distribution from which the random numbers in the sample data were drawn. The ecdf is similar in shape to the theoretical cdf, although it is not an exact match.

Instead, the ecdf is an exact match to the sample data. The ecdf is a discrete function, and is not smooth, especially in the tails where data might be sparse. You can smooth the distribution with Pareto tailsusing the paretotails function.

For more information and additional syntax options, see ecdf. To construct a continuous function based on cdf values computed from sample data, see Piecewise Linear Distribution.

A piecewise linear distribution estimates an overall cdf for the sample data by computing the cdf value at each individual point, and then linearly connecting these values to form a continuous curve. The circles represent each individual data point weight measurement. The black line that passes through each data point represents the piecewise linear distribution cdf for the sample data. A piecewise linear distribution linearly connects the cdf values calculated at each sample data point to form a continuous curve.

By contrast, an empirical cumulative distribution function constructed using the ecdf function produces a discrete cdf. For example, random numbers generated from the ecdf can only include x values contained in the original sample data. Random numbers generated from a piecewise linear distribution can include any x value between the lower and upper boundaries of the sample data. Because the piecewise linear distribution cdf is constructed from the values contained in the sample data, the resulting curve is often not smooth, especially in the tails where data might be sparse.

For information on how to work with a piecewise linear distribution, see Using PiecewiseLinearDistribution Objects.

Pareto tails use a piecewise approach to improve the fit of a nonparametric cdf by smoothing the tails of the distribution. You can fit a kernel distributionempirical cdfor a user-defined estimator to the middle data values, then fit generalized Pareto distribution curves to the tails. This technique is especially useful when the sample data is sparse in the tails.Updated 30 Dec Reliable and extremely fast kernel density estimator for one-dimensional data; Gaussian kernel is assumed and the bandwidth is chosen automatically; Unlike many other implementations, this one is immune to problems caused by multimodal densities with widely separated modes see example.

The estimation does not deteriorate for multimodal densities, because we never assume a parametric model for the data like those used in rules of thumb. INPUTS: data - a vector of data from which the density estimate is constructed; n - the number of mesh points used in the uniform discretization of the interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then n is rounded up to the next power of two, i.

Botev, J. Grotowski, and D. Kroese Annals of Statistics, Volume 38, Number 5, pages doi Zdravko Botev Retrieved April 19, This function is useful and fast to estimate the density and CDF, how can I obtain the PDF form such method, other than plot xmesh, density? Quick bug. If you only ask for one output the bandwidththe code throws an error. Is there any way to calculate any performance parameter of the distribution, i. Hello, Every body! Thanks, very useful.

Strangely I get very different results on Matlab b and b with the same data. On the recent version, the density distribution is more smooth and has a stronger tendency to not go to 0 at the ends of the distribution. I'm guessing this is due to changes in a Matlab function. Any ideas? I have encountered a problem with your implementation and seeking your help. The PDFs obtained using translated versions of the signal image histogram, in this case is not the same.

Hi Steven. It is the integral of the pdf function should be 1. So if your x-interval is very small, then the y-value of the pdf function could be larger than 1. Then y need to be to make the integral 1. I am using Botev tools and do not understand why the density function has values greater than one. I am knew to KDE and don't understand this yet. I figue a density function is suppose to add up to 1 when you integrate it? Hi all, I have a problem with pdf estimate that needs your help.Documentation Help Center.

The estimation is based on a product Gaussian kernel function. For univariate or bivariate data, use ksdensity instead. For example, you can define the function type that mvksdensity evaluates, such as probability density, cumulative probability, or survivor function.

You can also assign weights to the input values. The data measures the heat of hardening for 13 different cement compositions. The predictor matrix ingredients contains the percent composition for each of four cement ingredients. Estimate the kernel density for the first three observations in ingredients. Create a array of points at which to estimate the density. First, define the range and spacing for each variable, using a similar number of points in each dimension.

Next, use ndgrid to generate a full grid of points using the defined range and spacing. Finally, transform and concatenate to create an array that contains the points at which to estimate the density.

### Nonparametric Estimates of Cumulative Distribution Functions and Their Inverses

This array has one column for each variable. View the size of xi and f to confirm that mvksdensity calculates the density at each point in xi.

Sample data for which mvksdensity returns the probability density estimate, specified as an n -by- d matrix of numeric values. Data Types: single double. Points at which to evaluate the probability density estimate fspecified as a matrix with the same number of columns as x. The returned estimate f and pts have the same number of rows. Value for the bandwidth of the kernel-smoothing window, specified as a scalar value or d -element vector.

If bw is a scalar value, it applies to all dimensions. If you specify 'BoundaryCorrection' as 'log' default and 'Support' as either 'positive' or a two-row matrix, mvksdensity converts bounded data to be unbounded by using log transformation. The value of bw is on the scale of the transformed values. Example: 'Bandwidth',0. Specify optional comma-separated pairs of Name,Value arguments.

Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1, Boundary correction method, specified as the comma-separated pair consisting of 'BoundaryCorrection' and either 'log' or 'reflection'.

Then, it transforms back to the original bounded scale after density estimation. If you specify 'Support','positive'then mvksdensity applies log x j for each dimension, where x j is the j th column of the input argument x. For details, see Reflection Method. Example: 'BoundaryCorrection','reflection'. Function to estimate, specified as the comma-separated pair consisting of 'Function' and one of the following.

Example: 'Function''cdf'. Type of kernel smoother, specified as the comma-separated pair consisting of 'Kernel' and one of the following. You can also specify a kernel function that is a custom or built-in function.Documentation Help Center. The estimate is based on a normal kernel function, and is evaluated at equally-spaced points, xithat cover the range of the data in x. Here, xi and pts contain identical values.

For example, you can define the function type ksdensity evaluates, such as probability density, cumulative probability, survivor function, and so on. Or you can specify the bandwidth of the smoothing window. The default bandwidth is the optimal for normal densities. Estimate pdfs with two different boundary correction methods, log transformation and reflection, by using the 'BoundaryCorrection' name-value pair argument.

The default boundary correction method is log transformation. On the other hand, the reflection method does not cause undesirable peaks near the boundary.

An estimate with a smaller bandwidth might produce a closer estimate to the empirical cumulative distribution function. The ksdensity estimate with a smaller bandwidth matches the empirical cumulative distribution function better.

Create a logical vector that indicates censoring. Here, observations with lifetimes longer than 10 are censored. Generate a mixture of two normal distributions, and plot the estimated inverse cumulative distribution function at a specified set of probability values. A higher bandwidth further smooths the density estimate, which might mask some characteristics of the distribution.

A smaller bandwidth smooths the density estimate less, which exaggerates some characteristics of the sample. Generate a by-2 matrix containing random numbers from a mixture of bivariate normal distributions. Sample data for which ksdensity returns f values, specified as a column vector or two-column matrix. Use a column vector for univariate data, and a two-column matrix for bivariate data.

Data Types: single double. Points at which to evaluate fspecified as a vector or two-column matrix. For univariate data, pts can be a row or column vector. The length of the returned output f is equal to the number of points in pts. Axes handle for the figure ksdensity plots to, specified as a handle. For example, if h is a handle for a figure, then ksdensity can plot to that figure as follows.

Example: ksdensity h,x. Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1, The bandwidth of the kernel-smoothing window, which is a function of the number of points in xspecified as the comma-separated pair consisting of 'Bandwidth' and a scalar value.Documentation Help Center.

Kernel Distribution. A kernel distribution is a nonparametric representation of the probability density function of a random variable. Nonparametric and Empirical Probability Distributions. Fit Kernel Distribution Object to Data. Fit Kernel Distribution Using ksdensity. This example shows how to generate a kernel probability density estimate from sample data using the ksdensity function. Fit Distributions to Grouped Data Using ksdensity.

This example shows how to fit kernel distributions to grouped sample data using the ksdensity function. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.

Other MathWorks country sites are not optimized for visits from your location. Toggle Main Navigation. Search Support Support MathWorks. Search MathWorks. Off-Canvas Navigation Menu Toggle. Kernel Distribution Fit a smoothed distribution based on a kernel function and evaluate the distribution. Functions fitdist Fit probability distribution object to data distributionFitter Open Distribution Fitter app ksdensity Kernel smoothing function estimate for univariate and bivariate data mvksdensity Kernel smoothing function estimate for multivariate data.

Objects KernelDistribution Kernel probability distribution object. Topics Kernel Distribution A kernel distribution is a nonparametric representation of the probability density function of a random variable.

Nonparametric and Empirical Probability Distributions Estimate a probability density function or a cumulative distribution function from sample data. Fit Kernel Distribution Object to Data This example shows how to fit a kernel probability distribution object to sample data.

Fit Kernel Distribution Using ksdensity This example shows how to generate a kernel probability density estimate from sample data using the ksdensity function.